Class Network

bindsnet/network/network.py:31–491

Central object of the ``bindsnet`` package. Responsible for the simulation and interaction of nodes and connections. **Example:** .. code-block:: python import torch import matplotlib.pyplot as plt from bindsnet import encoding from bindsn

class Network(torch.nn.Module):
    # language=rst
    """
    Central object of the ``bindsnet`` package. Responsible for the simulation and
    interaction of nodes and connections.

    **Example:**

    .. code-block:: python

        import torch
        import matplotlib.pyplot as plt

        from bindsnet         import encoding
        from bindsnet.network import Network, nodes, topology, monitors

        network = Network(dt=1.0)  # Instantiates network.

        X = nodes.Input(100)  # Input layer.
        Y = nodes.LIFNodes(100)  # Layer of LIF neurons.
        C = topology.Connection(source=X, target=Y, w=torch.rand(X.n, Y.n))  # Connection from X to Y.

        # Spike monitor objects.
        M1 = monitors.Monitor(obj=X, state_vars=['s'])
        M2 = monitors.Monitor(obj=Y, state_vars=['s'])

        # Add everything to the network object.
        network.add_layer(layer=X, name='X')
        network.add_layer(layer=Y, name='Y')
        network.add_connection(connection=C, source='X', target='Y')
        network.add_monitor(monitor=M1, name='X')
        network.add_monitor(monitor=M2, name='Y')

        # Create Poisson-distributed spike train inputs.
        data = 15 * torch.rand(100)  # Generate random Poisson rates for 100 input neurons.
        train = encoding.poisson(datum=data, time=5000)  # Encode input as 5000ms Poisson spike trains.

        # Simulate network on generated spike trains.
        inputs = {'X' : train}  # Create inputs mapping.
        network.run(inputs=inputs, time=5000)  # Run network simulation.

        # Plot spikes of input and output layers.
        spikes = {'X' : M1.get('s'), 'Y' : M2.get('s')}

        fig, axes = plt.subplots(2, 1, figsize=(12, 7))
        for i, layer in enumerate(spikes):
            axes[i].matshow(spikes[layer], cmap='binary')
            axes[i].set_title('%s spikes' % layer)
            axes[i].set_xlabel('Time'); axes[i].set_ylabel('Index of neuron')
            axes[i].set_xticks(()); axes[i].set_yticks(())
            axes[i].set_aspect('auto')

        plt.tight_layout(); plt.show()
    """

    def __init__(
        self,
        dt: float = 1.0,